Image resizing using improved seam merging

Mishiba, Kazu; Ikehara, Masaaki

doi:10.1109/icassp.2012.6288118

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The final variation of seam carving that we explore is called seam merging [35]. This method merges a two-pixelwidth seam element into one new pixel during image reduction and inserts a new pixel between the two pixels during image enlargement.…”

Section: Other Seam Carving Methodsmentioning

confidence: 99%

SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

Gudavalli¹,

Erik²,

Nataraj³

et al. 2021

Preprint

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Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or insert/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. The proposed method is evaluated extensively on a large collection of images from different sources, demonstrating a high level of detection and localization performance across these datasets. The datasets curated during this work will be released to the public.

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Section: Other Seam Carving Methodsmentioning

confidence: 99%

SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

Gudavalli¹,

Erik²,

Nataraj³

et al. 2021

Preprint

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“…Due to the lack of use of pixel importance, the method could not safeguard salient contents. Hence, to enhance seam merging Mishiba et al [64] used importance and structure energies. When estimating the structure energy of the original image he tried to safeguard the main structures by taking a cartoon version of the source image.…”

Section: Content-aware Seam Carvingmentioning

confidence: 99%

A Survey on Content Aware Image Resizing Methods

Garg¹,

Negi²

2020

KSII TIIS

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With the advancement in the field of image processing, images are being processed using various image processing algorithms. Nowadays, many efficient content-aware image resizing techniques are being used to safeguard the prominent regions and to generate better results that are visually appealing and pleasing while resizing. Advancements in the new display device with varying screen size demands the development of efficient image resizing algorithm. This paper presents a survey on various image retargeting methods, comparison of image retargeting results based on performance, and also exposes the main challenges in image retargeting such as content preservation of important regions, distortion minimization, and improving the efficiency of image retargeting methods. After reviewing literature from researchers it is suggested that the use of the single operator in image retargeting such as scaling, cropping, seam carving, and warping is not sufficient for obtaining satisfactory results, hence it is essential to combine multiple image retargeting operators. This survey is useful for the researchers interested in content-aware image retargeting.

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“…The core goal of retargeting methods is to change the size of an image while limiting distortion in important regions. Seam carving [3] and its expansions [4]- [7] are one commonly used approach for image retargeting. Seam carving changes the size of a source image by iteratively carving out or inserting paths of pixels, which is called seam.…”

Section: Related Workmentioning

confidence: 99%

“…Several retargeting methods have been proposed, including seam carving [3]- [7] and image warping [8]- [13]. These methods, however, fail to avoid distortion in the case where visually important regions are distributed all over the image.…”

Section: Introductionmentioning

confidence: 99%

Content-Aware Image Retargeting Incorporated with Letterboxing

Mishiba

Oyamada

Kondo

2017

IEICE Trans. Inf. & Syst.

Self Cite

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SUMMARYConventional image retargeting methods fail to avoid distortion in the case where visually important regions are distributed all over the image. To reduce distortions, this paper proposes a novel image retargeting method that incorporates letterboxing into an image warping framework. Letterboxing has the advantage of producing results without distortion or content loss although being unable to use the entire display area. Therefore, it is preferable to combine a retargeting method with a letterboxing operator when displaying images in full screen. Experimental results show that the proposed method is superior to conventional methods in terms of visual quality measured by an objective metric.

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Image resizing using improved seam merging

Cited by 4 publications

References 7 publications

SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

A Survey on Content Aware Image Resizing Methods

Content-Aware Image Retargeting Incorporated with Letterboxing

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